An association network analysis among microeukaryotes and bacterioplankton reveals algal bloom dynamics. Issue 1 (9th January 2015)

• Record Type: Journal Article
• Authors: Tan, Shangjin; Zhou, Jin; Zhu, Xiaoshan; Yu, Shichen; Zhan, Wugen; Wang, Bo; Cai, Zhonghua; Lindell, D.
• Is Part Of: Journal of phycology. Volume 51:Issue 1(2015:Feb.)
• Page Start: 120
• Page End: 132
• Publication Date: 2015-01-09
• Subjects: Algae -- Periodicals; 579.8
• DOI: 10.1111/jpy.12259
• Languages: English
• ISSNs: 0022-3646

Abstract

<abstract abstract-type="main" id="jpy12259-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Algal blooms are a worldwide phenomenon and the biological interactions that underlie their regulation are only just beginning to be understood. It is established that algal microorganisms associate with many other ubiquitous, oceanic organisms, but the interactions that lead to the dynamics of bloom formation are currently unknown. To address this gap, we used network approaches to investigate the association patterns among microeukaryotes and bacterioplankton in response to a natural <italic>Scrippsiella trochoidea</italic> bloom. This is the first study to apply network approaches to bloom dynamics. To this end, terminal restriction fragment (T‐RF) length polymorphism analysis showed dramatic changes in community compositions of microeukaryotes and bacterioplankton over the blooming period. A variance ratio test revealed significant positive overall associations both within and between microeukaryotic and bacterioplankton communities. An association network generated from significant correlations between T‐RFs revealed that <italic>S. trochoidea</italic> had few connections to other microeukaryotes and bacterioplankton and was placed on the edge. This lack of connectivity allowed for the <italic>S. trochoidea</italic> sub‐network to break off from the overall network. These results allowed us to propose a conceptual model for explaining how changes in microbial associations regulate the dynamics of an algal bloom. In addition, key T‐RFs were screened by principal components analysis, correlation coefficients, and network analysis. Dominant T‐RFs were then identified through 18S and 16S rRNA gene clone libraries. Results showed that microeukaryotes clustered predominantly with <italic>Dinophyceae</italic> and <italic>Perkinsea</italic> while the majority of bacterioplankton identified were <italic>Alphaproteobacteria</italic>, <italic> Gammaproteobacteria, </italic> and <italic>Bacteroidetes</italic>. The ecologi‐cal roles of both were discussed in the context of these findings.</p> </abstract>